Telecommunication systems utilizing Wideband Code Division Multiple Access (WCDMA) continues to evolve to support high-bit rate applications. As the demand for higher data rates increases, however, greater self-interference from the dispersive radio channel limits performance. Consequently, advanced receivers for WCDMA terminal platforms and base stations have been and are continuously being developed.
Signals transmitted in a wireless communication system such as a Code Division Multiple Access (CDMA) or Wideband CDMA (WCDMA) system are subjected to multiple sources of interference and noise as they propagate via radio channels. The interference and noise components that affect signal transmission and reception in a wireless communication system are broadly referred to as impairments. Certain types of impairments may be correlated. That is, two seemingly independent signal impairments may in fact be related, and thus are said to be correlated. Some conventional receiver types such as the Generalized-RAKE (G-RAKE) receiver, see e.g. [1]-[3], and its Chips Equalizer (CEQ) counterpart use knowledge of impairment correlations to improve received signal quality. G-Rake receivers and CEQs also use an estimate of a multipath fading channel response in their received signal processing.
For example, a G-Rake receiver includes various signal “fingers” where each finger has an assigned path delay for receiving a particular image of a multipath signal and a correlator for despreading the received image. In combination, the signal fingers de-spread multiple signal images of a received multipath signal, this utilizing the multipath channel dispersion phenomenon. Additional “probing” fingers may be placed off path delays for capturing impairment correlations information. The finger outputs are weighted and coherently combined to improve received signal demodulation and/or received signal quality reception estimation, e.g. signal-to-interference (plus noise) (SIR) estimation. The processing weights assigned to the finger outputs are conventionally a function of the channel response and impairment correlations. As such, knowledge of signal impairments may be used to improve received signal processing. In a similar manner, CEQs utilize impairment correlations information for improving received signal processing where the selection of equalization filter taps in a CEQ is comparable to the placement of fingers in a G-Rake receiver and the generation of equalization filter coefficients is comparable to the generation of G-Rake combining weights.
Parametric G-Rake receivers estimate impairment correlations using a modeling approach. The model employs parameters, sometimes referred to as fitting parameters that can be estimated in a number of ways such as least-squares fittings. The parametric impairment correlations modeling process depends on corresponding model fitting parameters and on estimates of the channel response. However, signal impairments affect the channel response estimation process, particularly when the impairments are severe. As such, impairment correlation estimation and channel response estimation may be interdependent, particularly when interference is severe.
One specific type of receiver that has been developed is the so-called Rake receiver and the subsequently evolved Generalized-Rake or G-Rake receiver. In a Rake receiver signal energy is collected from different delayed versions of a transmitted signal. The channel response generates multiple images of the transmitted signal (that is the dispersive, multi-path channel gives rise to different versions). The “fingers” of the Rake receiver extract signal energy from delayed signal images by despreading and combining them. The Rake receiver coherently combines the finger outputs using complex conjugates of estimated channel coefficients to estimate the modulation symbol. Each despread value consists of a signal component, an interference component, and a noise component. When combining the values the Rake receiver aligns the signal components so that they add to one another, creating a larger signal component.
A G-Rake receiver operates in a similar, but slightly different manner. The G-Rake receiver uses fingers and combining techniques to estimate a symbol. However, the G-Rake uses extra interference fingers to collect information about interference on the signal fingers. This interference might result from other symbols of interest (self-interference) or symbols intended for other users in the cell (own-cell interference) or symbols intended for other users in other cells (other-cell interference). The extra fingers capture information about the interference. This is used to cancel interference on the signal fingers. In addition, a separate procedure is used to form combining weights. Rake receivers use a weighted sum of despread values to estimate symbols. Despread values are thus combined using combining weights. Besides estimating the channel, the G-Rake estimates the correlations between the impairment (interference plus noise) on different fingers. The correlation captures the “color” of the impairment. This information can be used to suppress interference. Channel estimates and impairment correlation estimates are used to form the combining weights. As a result, the combining process collects signal energy and suppresses interference. The G-Rake receiver combines two despread values to cancel interference and increase the signal component. By contrast, the Rake receiver solely maximizes the signal component.
In order to remain competitive on the market, WCDMA systems are constantly evolving and striving for higher bit rates. In order to achieve this, concepts like higher order modulation and MIMO are considered. However, to be able to benefit from all these new features better signal to noise ratio (SNR) conditions are required. Consequently, it is common for telecommunications systems to operate at significantly higher Ec/N0 regions, which make the interference situation more severe. Hence, in order to achieve the desired targets a good G-Rake becomes essential. Unfortunately, when moving to these higher Ec/N0 regions the implementation of the G-Rake becomes more sensitive, and the current G-Rake algorithm is in general not good enough. In particular, we need to improve the channel estimation procedure and especially the estimation of the covariance matrix required in the G-Rake.